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Support Vector Machines and Their Application in Chemistry and Biotechnology (Paperback)
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Support Vector Machines and Their Application in Chemistry and Biotechnology (Paperback)
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Support vector machines (SVMs) are used in a range of applications,
including drug design, food quality control, metabolic fingerprint
analysis, and microarray data-based cancer classification. While
most mathematicians are well-versed in the distinctive features and
empirical performance of SVMs, many chemists and biologists are not
as familiar with what they are and how they work. Presenting a
clear bridge between theory and application, Support Vector
Machines and Their Application in Chemistry and Biotechnology
provides a thorough description of the mechanism of SVMs from the
point of view of chemists and biologists, enabling them to solve
difficult problems with the help of these powerful tools. Topics
discussed include: Background and key elements of support vector
machines and applications in chemistry and biotechnology Elements
and algorithms of support vector classification (SVC) and support
vector regression (SVR) machines, along with discussion of
simulated datasets The kernel function for solving nonlinear
problems by using a simple linear transformation method Ensemble
learning of support vector machines Applications of support vector
machines to near-infrared data Support vector machines and
quantitative structure-activity/property relationship (QSAR/QSPR)
Quality control of traditional Chinese medicine by means of the
chromatography fingerprint technique The use of support vector
machines in exploring the biological data produced in OMICS study
Beneficial for chemical data analysis and the modeling of complex
physic-chemical and biological systems, support vector machines
show promise in a myriad of areas. This book enables
non-mathematicians to understand the potential of SVMs and utilize
them in a host of applications.
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